2017 NOAA NGS Topobathy Lidar DEM: St. Jeromes Creek, MD

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This graphic displays the footprint for this lidar data set.

Data for the St. Jeromes Creek project was acquired by NOAA using a Riegl VQ880G sensor. All delivered lidar data is referenced to:

Horizontal Datum-NAD83 (2011) epoch: 2010

Projection-UTM Zone 18 North

Horizontal Units-meters

Vertical Datum-NAD83(2011) epoch: 2010

Vertical Units-meters

This dataset encompasses 342 500m x 500m tiles. Both green lidar data and NIR lidar data were acquired.

NOAA acquired, calibrated, and performed the refraction correction of the lidar data. Light travels at different speeds in air versus water and its direction of travel or angle is changed or refracted when entering the water column. The refraction correction process corrects for this difference by adjusting the depth (distance traveled) and horizontal position (change of angle/direction) of the lidar data acquired within water.

Dewberry received the calibrated green and NIR data and verified complete coverage. Relative accuracy of the green swaths compared to overlapping and adjacent green swaths as well as the relative accuracy of green swaths compared to overlapping and adjacent NIR swaths was verified through the use of Delta-Z (DZ) orthos created in GeoCue software. Intraswath or within a swath relative accuracy was verified using Quick Terrain Modeler. Profiles of elevated planar features, such as roofs, were used to verify horizontal alignment between overlapping swaths.

Dewberry used algorithms in TerraScan to create the intial ground/submerged topography surface.

Dewberry created 2-D breaklines by digitizing breaklines in a 2-D environment using intensity imagery as a reference. Dewberry used the 2-D breaklines to classify the bathymetric bottom and ground points properly in TerraScan.

All lidar data was peer-reviewed. Dewberry's QAQC also included creating void polygons for use during review. All necessary edits were applied to the dataset. GeoCue software was used to update LAS header information, including all projection and coordinate reference system information. The final lidar data is in LAS format 1.2 and point data record format 3.

The final classificaton scheme is as follows:

1-Unclassified

2-Ground

7-Topographic Noise

22-Bathymetric Noise

25-Water Column

26-Bathymetric Bottom

27-Water Surface

30-International Hydrograpic Organization (IHO) S-57 Object

All data was then verified by an Independent QC department within Dewberry. The independent QC was performed by separate analysts who did not perform manual classification or editing. The independent QC involved quantitative and qualitative reviews.

Dewberry made a copy of the final lidar data and transformed the data from ellipsoid heights to orthometric heights, referenced to NAVD88 Geoid 12B, meters. The lidar data in orthometric heights was used to create the final topobathymetric DEMs in orthometric heights

Lidar data was converted to a LAS Dataset. Lidar data classified as ground (2) and submerged topography (26) were then used to generate a raster in IMG format with 1 meter pixel resolution. The output raster was created over the full AOI area to reduce edge-matching issues and improve seamlessness. The raster was clipped to the tile grid and named according to project specifications to result in tiled topobathymetric DEMs.

All tiled DEMs incorporate the use of the void polygons. The void polygons represent bathymetric areas with no bathymetric bottom returns and are set as NoData in the DEMs. Void polygon creation is described in the final project report and the void polygon metadata.

A confidence layer has been created and provided to NOAA as part of the deliverables. Except where there are not enough lidar data points in a pixel to calulate a standard deviation value, the confidence layer is a raster product in IMG format with 1 meter square pixels. The confidence layer provides a standard deviation value for every pixel by calculating the standard deviation of all ground and/or submerged topography lidar points that are located within a single pixel. The confidence layer pixels align to the pixels in the topobathy DEMs. The confidence layer can be displayed by unique values or classified into desired bins/ranges for analysis over larger areas.